Modelling for Science, for a better future - some recent outcomes

Identifying the Transition Zone Between East and West Dharwar Craton by Seismic Imaging

by Ashish and Imtiyaz A. Parvez

The data from 12 temporary broadband seismic stations operated across east–west corridor in Dharwar region of Indian Peninsula along with ten other seismic stations operated by CSIR National Geophysical Research Institute (NGRI) in the region have been analysed that provide high-resolution image of southern Dharwar crust. Crust along the corridor is imaged by receiver function H−kH−k stacking, common conversion point stacking using data from 22 sites in combination with joint inversion modeling of receiver functions and Rayleigh wave group velocity dispersion curves. The velocity image reveals thinner crust (36–38 km) except one site (coinciding with Cuddapah basin on the surface) in East Dharwar Craton (EDC), while crust beneath the West Dharwar Craton (WDC) is thicker (46–50 km). This study also observed a transition zone between EDC and WDC starting west of Closepet granite to the east of Chitradurga Schist Belt (CSB), which shows diffused Moho with a thickness of 40–44 km. Chitradurga Schist Belt is identified as the contact between Mesoarchean (WDC) and Neoarchean (EDC) crustal blocks. The lowermost part of the crust ( Vs>4.0Vs>4.0 ) is thin (2–6 km) beneath EDC, intermediate (6–8 km) beneath transition zone and thicker (14–30 km) beneath WDC across the profile.

Current computational resources and physical knowledge of the seismic wave generation and propagation processes allow for reliable numerical and analytical models of waveform generation and propagation. From the simulation of ground motion, it is easy to extract the desired earthquake hazard parameters. Accordingly, a scenario-based approach to seismic hazard assessment has been developed, namely the neo-deterministic seismic hazard assessment (NDSHA), which allows for a wide range of possible seismic sources to be used in the definition of reliable scenarios by means of realistic waveforms modelling. Such reliable and comprehensive characterization of expected earthquake ground motion is essential to improve building codes, particularly for the protection of critical infrastructures and for land use planning. Parvez et al. (Geophys J Int 155:489–508, 2003) published the first ever neo-deterministic seismic hazard map of India by computing synthetic seismograms with input data set consisting of structural models, seismogenic zones, focal mechanisms and earthquake catalogues. As described in Panza et al. (Adv Geophys 53:93–165, 2012), the NDSHA methodology evolved with respect to the original formulation used by Parvez et al. (Geophys J Int 155:489–508, 2003): the computer codes were improved to better fit the need of producing realistic ground shaking maps and ground shaking scenarios, at different scale levels, exploiting the most significant pertinent progresses in data acquisition and modelling. Accordingly, the present study supplies a revised NDSHA map for India. The seismic hazard, expressed in terms of maximum displacement (Dmax), maximum velocity (Vmax) and design ground acceleration (DGA), has been extracted from the synthetic signals and mapped on a regular grid over the studied territory.

An objective method is used for determining the rainfall threshold for identifying extreme rainfall events (EREs) over the urban city, Bangalore, using observed rainfall data for a period of 35 years (1971–2005). Using this threshold, 52 EREs were identified during the period 2010–2014 using high-resolution rain-gauge observations. From these EREs, 15 localized and non-localized events were chosen based on spatial distribution to examine the forecast skill of the Weather Research and Forecasting (WRF) model. Apart from the conventional verification methods, a number of skill scores and indices were defined for a comprehensive evaluation of rainfall model skill. In general, the forecast underpredicted the magnitude of localized and non-localized EREs for the majority of cases; however, the model overpredicted light rainfall (≤10 mm day−1). The model showed a success rate of 59% in simulating light rainfall for localized EREs while 12% of events were missed and 29% were wrongly predicted. The success rate was significantly reduced at higher rainfall categories for localized and non-localized EREs, where the forecast missed the majority of rainfall events. The Reliability Index (RI) computed clearly showed that model skill is relatively higher for non-localized EREs compared to localized EREs. The average forecast reliability for non-localized and localized EREs were 74 and 51%, respectively. For localized EREs, model skill is relatively higher in rainfall location prediction (61%) compared to area (44%) and intensity (46%) prediction; while in the case of non-localized EREs, model skill is similar for location, intensity and area prediction. It is found that coupling an urban canopy model with WRF reduces the model errors particularly for lower rainfall thresholds.

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